Executives across industries are pouring resources into artificial intelligence (AI), hoping to transform product development, manufacturing, and service. Yet, despite the hype, most of these projects never deliver on their promise. In fact, studies consistently report that up to 80% of AI projects fail to generate business value.
Why? It isn’t usually the algorithm’s fault. The root cause is something far more fundamental: data. Specifically, the lack of clean, structured, and contextualized product data.
Think of AI as the brain. Powerful, capable, and adaptive. But a brain can only act on the signals it receives. That’s where Product Lifecycle Management (PLM) comes in. PLM is the nervous system—the structured network that captures, organizes, and feeds reliable information into AI systems. Without it, AI in manufacturing and product development is built on shaky ground.
The Promise of AI in Product Development
Business leaders have high expectations for AI. From the boardroom to the shop floor, the vision is consistent:
- Faster time to market through automated design exploration and simulation.
- Lower costs by optimizing manufacturing processes and reducing service expenses.
- Better customer experience with more reliable products and predictive service models.
- Greater innovation capacity with generative design and digital twin simulations.
In manufacturing, the potential of AI is especially compelling. Predictive maintenance can reduce downtime by up to 30%. AI-driven scheduling can maximize throughput without additional capital expense. Digital twins (virtual replicas of products and systems) can help engineers anticipate performance issues long before physical prototypes are built.
The promise is real. But the path is filled with risk. Too often, organizations chase these outcomes without first addressing the foundation: their product data.
Why AI Fails Without PLM
Despite big investments, many AI initiatives stall or collapse because the underlying data is incomplete, inconsistent, or scattered across silos. Consider a simple example:
A customer support chatbot designed to answer product questions. If the bot’s knowledge base only contains marketing descriptions but not the latest engineering specifications, it will inevitably give wrong answers. Or imagine training a predictive maintenance algorithm on machine data that isn’t tied back to specific product configurations. The results will be unreliable at best—and misleading at worst.
AI without PLM is like trying to build a skyscraper on sand. No matter how strong your construction materials, the foundation won’t hold.
The problem lies in how product information is typically stored. Engineering drawings live in CAD tools. Bills of materials are locked in ERP systems. Manufacturing instructions sit in MES. Service records and technical publications often exist in entirely separate repositories. AI systems fed on these fragmented, unstructured datasets can’t produce accurate insights.
Worse, without a structured digital thread connecting data across the product lifecycle, there’s no way to maintain traceability. In regulated industries—like aerospace, automotive, or medical devices—this isn’t just inefficient. It’s a compliance risk.
PLM: the Backbone of AI Readiness
This is where PLM for AI comes into play. A modern PLM platform does more than manage CAD files. It serves as the single source of truth for all product-related information, spanning:
- Designs, parts, and assemblies
- Engineering change orders and requirements
- Manufacturing processes and instructions
- Service documentation and field data
- Technical publications, compliance records, and testing results
By centralizing this data, PLM creates a structured, contextualized foundation that AI can trust. Every piece of information is tied to its source, version-controlled, and connected across the product lifecycle.
In practice, PLM acts as the digital backbone that feeds AI systems:
- PLM (designs, requirements, service records) →
- Digital Thread (context, traceability, connections) →
- AI / Machine Learning (predictive models, generative algorithms, simulations)
The result? Instead of acting on fragmented inputs, AI systems gain access to accurate, contextualized product data. This allows companies to realize the true potential of AI in manufacturing—whether that’s predictive maintenance, smarter design automation, or faster regulatory approvals.
Roadmap to Success
Preparing your organization for AI isn’t about jumping into the latest algorithm. It’s about laying the right PLM foundation. Here’s a practical roadmap for executives:
- Clean Up Product Data
- Audit existing sources. Eliminate duplicates, outdated versions, and unstructured repositories.
- Connect Core Systems
- Integrate PLM with ERP, MES, CRM, and IoT platforms. Create a continuous flow of information.
- Enable the Digital Thread
- Establish traceability across the lifecycle—linking requirements to parts, test results, and service records.
- Prepare Data for AI
- Structure and contextualize product data so it’s machine-readable and reliable.
With this roadmap, quick wins become possible:
- Predictive Maintenance: AI trained on PLM-managed product data and IoT sensor streams can anticipate equipment failures and reduce unplanned downtime.
- Generative Design: Engineers can leverage AI tools that draw from validated PLM data (materials, constraints, performance history) to explore optimal product configurations.
- Compliance Automation: AI models can scan PLM-managed documentation to flag compliance risks, reducing the burden of audits.
These examples show that AI’s promise in manufacturing isn’t futuristic—it’s happening now. But only for companies that take PLM seriously.
If You’re Serious About AI, Start With PLM
AI has the power to revolutionize product development and manufacturing. But the statistics don’t lie: most AI projects fail to deliver value. The missing link isn’t more advanced algorithms—it’s structured, reliable product data.
PLM provides that foundation. By serving as the single source of truth and enabling a connected digital thread, PLM ensures your AI initiatives are built on solid ground.
If your organization is serious about AI, it’s time to assess your PLM maturity. Start by cleaning up product data, connecting systems, and enabling traceability. From there, AI will stop being a risky experiment and start becoming a strategic advantage.
